arshai

v1.13.0 safe
4.0
Medium Risk

A powerful agent framework for building conversational AI systems

🤖 AI Analysis

Final verdict: SAFE

The package shows some signs of obfuscation and uses non-HTTPS links, but these alone do not strongly indicate malicious intent. Given the absence of clear risks like shell execution or credential harvesting, the overall risk is considered low.

  • moderate obfuscation risk
  • non-secure network links
Per-check LLM notes
  • Network: The package makes network calls which are common for fetching external resources, but further investigation is needed to confirm legitimacy.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of base64 decoding on image and PDF data may indicate an attempt to obfuscate the source code, but it could also be for legitimate purposes such as data transmission.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The presence of non-HTTPS links suggests potential unsecured communication, but the lack of other red flags and a single package from a new maintainer indicates moderate risk.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://arshai.readthedocs.io
  • Detailed PyPI description (94986 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 269 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in nimunzn/arshai
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • ... ) """ with urllib.request.urlopen(url, timeout=timeout) as response: return ba
  • data URL prefix).""" with urllib.request.urlopen(url, timeout=timeout) as response: return ba
  • safe_http_client = httpx.Client( limits=limits_config, timeo
  • safe_http_client = httpx.Client( limits=httpx.Limits( ma
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • 2.4) img_bytes = base64.b64decode(img_data) parts.append(Part.from_bytes(data=img_
  • 1)[1] pdf_bytes = base64.b64decode(pdf_data) parts.append(Part.from_bytes(data=pdf_
  • he new module __import__(new_name) module = sys.modules[new_name]
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://jaeger:14268/api/traces
  • Non-HTTPS external link: http://jaeger:14268
  • Non-HTTPS external link: http://otel-collector:4317
✓ Git Repository History

Repository nimunzn/arshai appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Nima Nazarian" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with arshai 
Create a personalized health advisor chatbot using the 'arshai' package. This chatbot will help users manage their daily health routines by providing advice on diet, exercise, and sleep habits. The application should be designed to engage in natural conversations with users, understand their goals, and provide tailored recommendations based on their responses. Here are the steps and features to implement:

1. **Setup**: Install the 'arshai' package and set up a new project directory.
2. **User Onboarding**: Design a conversational flow where the user introduces themselves and sets their health goals (e.g., weight loss, muscle gain).
3. **Daily Check-ins**: Implement a feature where the chatbot asks users about their daily activities, including meals consumed, exercises performed, and hours of sleep.
4. **Advice Generation**: Use 'arshai' to create agents that analyze the user's inputs and generate personalized advice. For example, if a user mentions eating junk food, the agent should suggest healthier alternatives.
5. **Motivational Quotes**: Incorporate a feature where the chatbot shares motivational quotes or tips to keep the user motivated towards their health goals.
6. **Progress Tracking**: Enable users to track their progress over time. The chatbot should periodically review the user's inputs and provide feedback on their progress.
7. **Integration**: Optionally, integrate with external APIs (like nutrition databases) to enhance the advice provided by the chatbot.
8. **Testing and Feedback**: Test the chatbot with different scenarios and gather feedback from users to improve the conversation flows and advice generation.

This project leverages the 'arshai' package to handle complex conversational logic, ensuring that the chatbot can adapt its responses based on the context and history of interactions with the user.

💬 Discussion Feed

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